With the increasing usage of Unmanned Aerial Vehicle (UAV)s and the prediction of becoming applicable to more industries within the next decade there is a need for a controlling authority in the lower airspace. An Unmanned Aircraft System Traffic Man- agement (UTM) provides multiple solutions to how such a system should operate and what services it should provide. This makes a UTM a key infrastructure that will need to withstand potential cyberattacks and ensure safe communication channels with sensitive information. This thesis will provide an analysis of what key areas need protection and show an example of how to implement it in UTM50.  Possible vulnerabilities were identified by performing a risk analysis based on the Con- trolled Object-Oriented Risk Assessment (CORAS) model, and a comparison was made between similar communication systems to compare what challenges they face. To handle the vulnerabilities, countermeasures were implemented in UTM50 using coding libraries such as ZeroMQ and CurveZMQ. The implementation was tested to ensure its effective- ness against possible cyber attacks, and the traffic was monitored using Wireshark.  Finally, this thesis presents a few areas that require further research to ensure full safety and security across all communication channels.

Society today brings a high pace development and demand of Artificial intelligence systems as well as robotics. To further expand and to take one step closer to have Unmanned Aerial Vehicles (UAVs) working in the cities, the European Union Aviation Safety Agency launched a project that introduces U-space airspace, an airspace where UAVs, for instance, are allowed to operate for commercial services.The problems defined for U-space airspace resemble problems defined in the area of multi-agent path finding, such as scaling and traffic etc., resulting an interest to research whether MAPF-solutions can be applied to U-space scenarios. The following thesis extends the state-of-the-art MAPF-algorithm Continuous-time Conflict based search (CCBS) to handle simplified U-space scenarios, as well as extend other A*-based algorithms, such as a version of the Receding Horizon Lattice-based Motion Planning named Extended Multi-agent A* algorithm with Wait-Time (EMAWT) and an extended A* named Extended Multi-agent A* algorithm (EMA) to handle them. Comparisons of the three algorithms resulted in the EMAWT being the most reliable and stable solution throughout all tests, whilst for fewer agents, the CCBS being the clear best solution.